Novel Intelligent Traffic Light Controller Design

Abstract

Efficient traffic flow management at intersections is vital for optimizing urban transportation networks. This paper presents a comprehensive approach to refining traffic flow by analyzing the capacity of roads and integrating fuzzy logic-based traffic light control systems. We examined the capacity of roads connecting intersections, considering factors such as road vehicle capacity, vehicle speed, and traffic flow volume, through detailed mathematical modeling and analysis. Control is determined by the maximum capacity of each road segment, providing valuable insights into traffic flow dynamics. Building upon this capacity and flow analysis, the research introduces a novel intelligent traffic light controller (ITLC) system based on fuzzy logic principles. By incorporating real-time traffic data and leveraging fuzzy logic algorithms, our ITLC system dynamically adjusts traffic light timings to optimize vehicle flow at two intersections. The paper discusses the design and implementation of the ITLC system, highlighting its adaptive capabilities in response to changing traffic conditions. Simulation results demonstrate the effectiveness of the ITLC system in improving traffic flow and reducing congestion at intersections. Furthermore, this research provides an analysis of the mathematical models used to calculate road capacity, offering insights into the underlying principles of traffic flow optimization. Through the simulation, we have validated the accuracy and reliability of our controller.